MétaCan
Menu
Back to cohort

The transcription factor E2F1 promotes dopamine‐evoked neuronal apoptosis by a mechanism independent of transcriptional activation

2001· article· en· W1563363326 on OpenAlexaff
Sheng‐Tao Hou, Emily Cowan, Teena Walker, Nick Ohan, Mike Dove, Ingrid Rasqinha, John P. MacManus

Bibliographic record

VenueJournal of Neurochemistry · 2001
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsNational Research Council CanadaInstitute for Biological Sciences
Fundersnot available
KeywordsE2F1TransactivationEctopic expressionTranscription factorBiologyCell biologyGeneGenetics

Abstract

fetched live from OpenAlex

The E2F1 transcription factor plays an important role in promoting neuronal apoptosis; however, it is not clear how E2F1 does this. Here we show that E2F1 is involved in dopamine (DA)-evoked apoptosis in cerebellar granule neurons (CGNs). E2F1 -/- CGNs and CGNs expressing an antisense E2F1 cDNA were significantly protected from DA-toxicity relative to controls. The neuronal protection was accompanied by significantly reduced caspase 3 activity. E2F1-mediated neuronal apoptosis did not require activation of gene transcription because: (1) ectopic expression of E2F1 or its mutants lacking the transactivation domain induced neuronal apoptosis, whereas an E2F1 mutant lacking the DNA-binding domain did not; (2) under all of these conditions, known E2F1 target genes including cyclin A, cdc2 and p19(ARF) were not induced; and (3) DA-evoked neuronal apoptosis was associated with up-regulated E2F1, but not transcription of its target genes. Finally, E2F1-mediated neuronal apoptosis was associated with reduced nuclear factor (NF)-kappaB DNA-binding activity. Taken together, these data suggest that E2F1 promotes DA-evoked caspase 3-dependent neuronal apoptosis by a mechanism independent of gene transactivation, and this may possibly occur through inhibition of anti-apoptotic genes including NF-kappaB.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.237
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations57
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueJournal of NeurochemistrySame topicCancer-related Molecular PathwaysFrench-language works237,207